mk:swarmbook06

Summary

Data Swarm Clustering. Christian Veenhuis and Mario Köppen. In Swarm Intelligence in Data Mining, pages 221-241.Springer Berlin / Heidelberg, , 2006. (URL)

Abstract

Data clustering is concerned with the division of a set of objects into groups of similar objects. In social insects there are many examples of clustering processes. Brood sorting observed in ant colonies can be considered as clustering according to the developmental state of the larvae. Also nest cleaning by forming piles of corpse or items is another example. The observed sorting and cluster capabilities of ant colonies have already been the inspiration of ant-based clustering algorithms. Another kind of clustering mechanism can be observed in flocks of birds. In some rain-forests mixed-species flocks of birds can be observed. From time to time different species of birds are merging to become a multi-species swarm. The separation of multi-species swarm into single species can be considered as a kind of species clustering.This chapter introduces a data clustering algorithm based on species clustering. It combines methods of Particle Swarm Optimization and Flock Algorithms. A given set of data is interpreted as a multi-species swarm which wants to separate into single-species swarms, i.e. clusters. The data to be clustered are assigned to datoids which form a swarm on a two-dimensional plane. A datoid can be imagined as a bird carrying a piece of adat on its back. While swarming, this swarm divides into sub swarms moving over the plane and consisting of datoids carrying similar data. After swarming, these sub swarms of datoids can be grouped together as clusters.

Bibtex entry

@INCOLLECTION { mk:swarmbook06,
    ABSTRACT = { Data clustering is concerned with the division of a set of objects into groups of similar objects. In social insects there are many examples of clustering processes. Brood sorting observed in ant colonies can be considered as clustering according to the developmental state of the larvae. Also nest cleaning by forming piles of corpse or items is another example. The observed sorting and cluster capabilities of ant colonies have already been the inspiration of ant-based clustering algorithms. Another kind of clustering mechanism can be observed in flocks of birds. In some rain-forests mixed-species flocks of birds can be observed. From time to time different species of birds are merging to become a multi-species swarm. The separation of multi-species swarm into single species can be considered as a kind of species clustering.This chapter introduces a data clustering algorithm based on species clustering. It combines methods of Particle Swarm Optimization and Flock Algorithms. A given set of data is interpreted as a multi-species swarm which wants to separate into single-species swarms, i.e. clusters. The data to be clustered are assigned to datoids which form a swarm on a two-dimensional plane. A datoid can be imagined as a bird carrying a piece of adat on its back. While swarming, this swarm divides into sub swarms moving over the plane and consisting of datoids carrying similar data. After swarming, these sub swarms of datoids can be grouped together as clusters. },
    AUTHOR = { Christian Veenhuis and Mario Köppen },
    BOOKTITLE = { Swarm Intelligence in Data Mining },
    ADDED = { 2007-01-13 15:13:32 +0900 },
    MODIFIED = { 2008-02-28 12:16:59 +0900 },
    EDITOR = { Ajith Abraham and Crina Grosan and Vitorino Ramos },
    HASABSTRACT = { Yes },
    M3 = { 10.1007/978-3-540-34956-3{\_}10 },
    PAGES = { 221--241 },
    PDF = { sidm06.pdf },
    PUBLISHER = { Springer Berlin / Heidelberg },
    SERIES = { Studies in Computational Intelligence },
    TITLE = { Data Swarm Clustering },
    TY = { BOOK },
    URL = { http://dx.doi.org/10.1007/978-3-540-34956-3_10 },
    VOLUME = { 34/2006 },
    YEAR = { 2006 },
    1 = { http://dx.doi.org/10.1007/978-3-540-34956-3_10 },
}

On small computer displays, you can hide this right bar by using the 'Hide' button above.

News

Next conferences COMPSAC 2014 (Vasteras, Sweden, July 2014), INCoS-2014 (Salerno, Italy, September 2014).

New edited book "Soft Computing in Industrial Applications", V. Snasel, P. Kroemer, M. Koeppen, G. Schaefer, Springer AISC 223, July 2013.